1. Field of the Invention
This invention relates to a method and an apparatus for matching a sample texture to those contained in a large collection of images; and, more particularly, to a method and an apparatus which matches texture patterns independently of the intensities, scales, and orientations of the patterns.
2. Description of Prior Art
Texture relates to a human's perception of visual characteristics such as smoothness, coarseness, and regularity of various materials. Many objects, such as brick walls, bushes, roof tiles, and fabric, can be recognized or recalled based on the distinctive texture patterns the objects contain. Therefore, texture is a very important visual cue for retrieving user-required images from large image databases.
In general, a texture matching technique for image retrieval compares a query texture image with those contained in a plurality of database images, and retrieves those database images which contain one or more texture patterns that are similar to the query texture. There are generally three conventional approaches for performing texture matching or classification, namely, the structural approach, the statistical approach, and the spectral approach.
The structural approach characterizes a texture by determining the spatial arrangement of visual primitives such as line segments, line ends, and blobs of pixels. Although there is psychological evidence supporting such a structural approach, it is difficult from a computational standpoint to determine the visual primitives and their spatial arrangements.
The statistical approach characterizes a texture in terms of numerical attributes such as local statistics and simultaneous regression. These attributes describe the statistical distribution of intensity values around a pixel. Although these numerical attributes are generally easy to compute, they do not provide a simple means of performing scale- and orientation-invariant texture matching.
The spectral approach filters texture images using a set of filters, and uses the filtered outputs as features for texture classification. Gabor filters are most commonly used for this purpose. Gabor filters can extract frequency and orientation information from the texture images. The Gabor filtering approach has been used in W. Y. Ma and B. S. Manjunath, "Texture Features and Learning Similarity," Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pages 1160-1169, 1996; German Patent DE 4406020; and Japanese Patent JP 09185713.
Except for the work of the present Inventors, the above-mentioned prior techniques, however, do not provide a texture matching method that is invariant to both scale and orientation. Furthermore, such existing texture matching methods assume that each image contains only a single uniform texture pattern.